Evaluation of the BD urine culture application for artificial intelligence-assisted urine culture interpretation.

Authors

Petrus van der Walt, Prenilla Naidu, Silvana Avoledo, Cecilia Mak, Francene Starko

Published in

Journal of clinical microbiology. Pages e0027026. Jun 22, 2026. Epub Jun 22, 2026.

Abstract

Artificial intelligence-based culture reading tools can potentially accelerate reading, improving reporting consistency in image-based interpretation, which is relevant for high-volume urine cultures. This study evaluated the BD Kiestra Urine Culture Application (UCA) in a centralized microbiology laboratory. Consecutive midstream and indwelling catheter urine specimens inoculated onto BD BBL CHROMagar Orientation were analyzed by the UCA. Growth score output at different reporting thresholds was evaluated, along with expert rule-based categories constructed using the app's growth score, purity score, and presumptive identification parameters, with blinded technologist image reads as the comparator. Of 1,304 cultures, 1,251 were evaluable for growth score comparison, and 1,239 for expert-rule assessment. The exact agreement across four growth categories (<10⁵, 10⁵, 10⁶, ≥10⁷ CFU/L) was 86.2%. Using a <10⁶ CFU/L triage threshold, the overall agreement was 95.9%, with positive and negative percent agreement of 89.9% and 98.9%, respectively. In this dataset, 30.5% of cultures met this threshold for potential auto-release or batch-review. For presumptive identification in ≥10⁷ CFU/L pure or predominant cultures, UCA performed best for E. coli (PPA 98.9%). For Enterococcus spp., PPA was 89.5%, but PPV was low (25.4%), reflecting frequent false-positive presumptive calls. UCA performed well at clinically relevant thresholds and could meaningfully reduce the burden of manual reading. Most discrepancies reflected differences between total colony count outputs and morphotype-based laboratory quantitation conventions, along with missed subtle growth. Expert-rule category discrepancies were primarily related to differences in purity output and presumptive identification, particularly fine light-blue growth being called presumptive Enterococcus spp., highlighting a priority area for improvement.IMPORTANCEUrine cultures are among the most frequently ordered culture tests in clinical microbiology. Timely, consistent plate interpretation impacts both patient care and laboratory capacity. Digital imaging of culture plates facilitates the application of artificial intelligence to urine culture specimens, but laboratories need clear evidence of where these tools perform reliably and where their outputs require caution. This study shows how an automated urine culture reading application can support safe triage of cultures with little or no growth, helping technologists focus on specimens that likely require workup and clinical action. At the same time, it highlights an important limitation: organism labeling can be vulnerable to look-alike growth patterns. In our dataset, fine or hazy, light-blue growth that was not typical of Enterococcus spp. was classified by the application as presumptive Enterococcus spp., a mismatch that could affect downstream decisions if not recognized. These findings reinforce that local validation and human oversight remain essential, even for common uropathogens, such as Escherichia coli.

PMID:
42324609
Bibliographic data and abstract were imported from PubMed on 22 Jun 2026.

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